Name
Abstract | ||
|---|---|---|
We consider a distributed constrained convex optimization problem over a multi-agent (no central coordinator) network. We propose a completely decentralized and asynchronous gossip-based random projection (GRP) algorithm that solves the distributed problem using only local communications and computations. We analyze the convergence properties of the algorithm for a diminishing and a constant stepsize which are uncoordinated among agents. For a diminishing stepsize, we prove that the iterates of all agents converge to the same optimal point with probability 1. For a constant stepsize, we establish an error bound on the expected distance from the iterates of the algorithm to the optimal point. We also provide simulation results on a distributed robust model predictive control problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TAC.2015.2460051 | IEEE Transactions on Automatic Control |
Keywords | DocType | Volume |
Convergence,Optimization,Random variables,Robustness,Algorithm design and analysis,Clocks,Projection algorithms | Journal | abs/1304.1757 |
Issue | ISSN | Citations |
4 | 0018-9286 | 19 |
PageRank | References | Authors |
0.75 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soo-Min Lee | 1 | 148 | 12.00 |
| Angelia Nedic | 2 | 2323 | 148.65 |